Probabilistic machine learning refers to a category of techniques in the machine learning domain that deal with uncertainty in predictions. It contrasts with deterministic models, where the same input will always produce the same output.
In probabilistic models, uncertainty is captured and represented using probabilities. This can be helpful in many real-world situations where the data may be noisy or incomplete, or where a model’s prediction needs to be accompanied by a measure of confidence.
A common approach in probabilistic machine learning is to use Bayesian inference. Bayesian methods allow us to update our beliefs about unknown parameters using observed data and prior beliefs. This is typically achieved by computing the posterior distribution of the unknown parameters, given the observed data.
Probabilistic graphical models, such as Bayesian networks, Hidden Markov Models, and Gaussian Mixture Models, are popular tools in this space. Libraries like TensorFlow Probability and Pyro enable easy implementation and experimentation with these methods.
Probabilistic models can be applied in various domains, such as natural language processing, finance, healthcare, and more, providing a robust way to make predictions that are well-calibrated to uncertainty.